Soberfolks: On-Demand Driver Allocation System for Safe Personal Vehicle Mobility

Abstract

Urban mobility platforms primarily focus on transporting passengers rather than enabling individuals to safely use their own vehicles when they are temporarily unable to drive due to impairment, fatigue, or medical constraints. This paper presents SoberFolks, an on-demand driver allocation system that dispatches verified drivers equipped with foldable electric scooters to operate users’ personal vehicles.
The system integrates geohash-based spatial indexing, Haversine distance computation, and a queue-based driver allocation strategy to minimize assignment latency while ensuring fairness and scalability. Implemented using a distributed client-server architecture with secure authentication and real-time tracking, the framework demonstrates improved driver discovery efficiency compared to naive proximity search approaches. The proposed model introduces a novel paradigm in urban mobility by combining micro-mobility logistics with ride assistance services.